Development of a deep learning-based group contribution framework for targeted design of ionic liquids
<p dir="ltr">In this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property pr...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513542643777536 |
|---|---|
| author | Sadah Mohammed (18192859) |
| author2 | Fadwa Eljack (3333444) Monzure-Khoda Kazi (17191207) Mert Atilhan (1272906) |
| author2_role | author author author |
| author_facet | Sadah Mohammed (18192859) Fadwa Eljack (3333444) Monzure-Khoda Kazi (17191207) Mert Atilhan (1272906) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sadah Mohammed (18192859) Fadwa Eljack (3333444) Monzure-Khoda Kazi (17191207) Mert Atilhan (1272906) |
| dc.date.none.fl_str_mv | 2024-07-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compchemeng.2024.108715 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Development_of_a_deep_learning-based_group_contribution_framework_for_targeted_design_of_ionic_liquids/29715437 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering Information and computing sciences Machine learning Machine learning Deep learning Group contribution Computer-Aided Molecular Design Ionic liquids CO2 capture |
| dc.title.none.fl_str_mv | Development of a deep learning-based group contribution framework for targeted design of ionic liquids |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property predictions in a data-driven manner. Our proposed framework consists of two essential steps: establishing a correlation between IL viscosity and CO<sub>2</sub> solubility by merging two deep learning models (DNN-GC and ANN-GC) and utilizing this correlation to identify the optimal IL structure with maximal CO<sub>2</sub> absorption capacity. Our model achieves high accuracy with R2 values of 95%, 94.2%, and 96.4% for DNN-GC, ANN-GC, and DNN-ANN-GC, respectively. Correlation results align with the experimental data, affirming the applicability of our framework. Finally, the algorithm is employed in a CO<sub>2</sub> capture case study to generate and select the best-performing novel ILs, which exhibit behavior consistent with established ILs in the literature.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers & Chemical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compchemeng.2024.108715" target="_blank">https://dx.doi.org/10.1016/j.compchemeng.2024.108715</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6cc92f228a6868853765727075e64f1b |
| identifier_str_mv | 10.1016/j.compchemeng.2024.108715 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29715437 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Development of a deep learning-based group contribution framework for targeted design of ionic liquidsSadah Mohammed (18192859)Fadwa Eljack (3333444)Monzure-Khoda Kazi (17191207)Mert Atilhan (1272906)EngineeringChemical engineeringInformation and computing sciencesMachine learningMachine learningDeep learningGroup contributionComputer-AidedMolecular DesignIonic liquidsCO2 capture<p dir="ltr">In this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property predictions in a data-driven manner. Our proposed framework consists of two essential steps: establishing a correlation between IL viscosity and CO<sub>2</sub> solubility by merging two deep learning models (DNN-GC and ANN-GC) and utilizing this correlation to identify the optimal IL structure with maximal CO<sub>2</sub> absorption capacity. Our model achieves high accuracy with R2 values of 95%, 94.2%, and 96.4% for DNN-GC, ANN-GC, and DNN-ANN-GC, respectively. Correlation results align with the experimental data, affirming the applicability of our framework. Finally, the algorithm is employed in a CO<sub>2</sub> capture case study to generate and select the best-performing novel ILs, which exhibit behavior consistent with established ILs in the literature.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers & Chemical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compchemeng.2024.108715" target="_blank">https://dx.doi.org/10.1016/j.compchemeng.2024.108715</a></p>2024-07-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compchemeng.2024.108715https://figshare.com/articles/journal_contribution/Development_of_a_deep_learning-based_group_contribution_framework_for_targeted_design_of_ionic_liquids/29715437CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297154372024-07-07T03:00:00Z |
| spellingShingle | Development of a deep learning-based group contribution framework for targeted design of ionic liquids Sadah Mohammed (18192859) Engineering Chemical engineering Information and computing sciences Machine learning Machine learning Deep learning Group contribution Computer-Aided Molecular Design Ionic liquids CO2 capture |
| status_str | publishedVersion |
| title | Development of a deep learning-based group contribution framework for targeted design of ionic liquids |
| title_full | Development of a deep learning-based group contribution framework for targeted design of ionic liquids |
| title_fullStr | Development of a deep learning-based group contribution framework for targeted design of ionic liquids |
| title_full_unstemmed | Development of a deep learning-based group contribution framework for targeted design of ionic liquids |
| title_short | Development of a deep learning-based group contribution framework for targeted design of ionic liquids |
| title_sort | Development of a deep learning-based group contribution framework for targeted design of ionic liquids |
| topic | Engineering Chemical engineering Information and computing sciences Machine learning Machine learning Deep learning Group contribution Computer-Aided Molecular Design Ionic liquids CO2 capture |